Recently, the neural activities have been investigated to understand the relationship between the neurons and the mental and physical activities. In order to diagnose disease, such as Parkinson's disease, or to establish a direct interface between brain and external devices, many neural interface systems have been proposed and implemented. Among many other goals of these systems, the ability to continuously record neural signals from awake-behaving animals and humans has been one of the most important goals in neuroscience and neurophysiology. The development and optimization of MEMS and microfabrication technologies has contributed a major part in developing biocompatible, fully-implantable systems that can record from group of neurons up to a single-neuron recording systems. However, more challenges need to be addressed, especially to target the tissue-electrode interface, brain injury due to head movements in addition to power consumption of stand-alone multi-channel systems. On the other hand, old techniques such as electroencephalogram (EEG) typically do not satisfy the requirements of current neuroscience studies for successful diagnosis and treatment of central nervous system (CNS) disorders, neural-based prosthetics, and brain-machine interfaces.
The neural potentials can be categorized by the four primary different signals: single unit action potential (SUAP), local field potentials (LFP), electrocorticogram (ECoG) and scalp electroencephalogram (EEG) according to its sensing locations. All of these methods attempt to record μV-level extracellular potentials generated in the cerebral cortical layers. However, each method varies in its relative invasiveness as well as its spatial and spectral frequency. Generally, there is a trade-off between these parameters; the more invasive the recording technique, the higher the spatial and spectral frequency content of the recorded signal. As the spatial/spectral frequency content increases, so does the amount of information gained from the brain recordings. FIG. 1 depicts a partial cross-section of the head 30 showing the various layers at which different recording approaches are used. The outer layer 32 is the skin where scalp EEG measurements are made. Below that is the skull 34 followed by meninges 36, including the dura mater where epidural recordings are taken. Under the dura mater, ECoG measurements are taken at the surface of the cortex 38. Finally, LFP and Single Unit (Spike) recordings are made within the cortex 38.
A single unit action potential provides the most precise neural activity information. The signal is recorded from single neuron under the cortex, and could provide the 0.2 mm spatial resolution with up to 10 kHz in bandwidth, and 1 mV in amplitude. However, in order to reach the single neuron using micro-machined electrodes, the system is totally invasive and easily infects neural tissues. On the other hand, the scalp EEG potential is obtained on the surface of the scalp. Even though the scalp EEG system, a so-called non-invasive system, is the safest system due to non-penetration of the cranium, the bio-information with which the EEG system could provide is very limited in time and space because the scalp EEG system can only detect the ensemble activities of a number of neurons. Furthermore, in the EEG system, the chronic monitoring in free movement is limited by many external cables and devices. In the last few years, ECoG has gained popularity among researchers as the most pragmatic method for long-term chronic brain monitoring. ECoG system records the brain activities on the surface of the cortex penetrating the meninges which is the brain protection membranes under the cranium: the dura mater, the arachnoid mater, and the pia mater. The ECoG system is less invasive than the action potential system, and can provide better accuracy of bio-information than the surface EEG signal with 5 mm in spatial resolution, 500 μV in amplitude, and up to 250 Hz in bandwidth. However, in spite of these advantages, this system still has some limitations such as large system volume and safety issues because currently a passive electrode array is implanted by opening a 2 cm hole in skull by craniotomy and tethered with a bundle of wires for data transmission.
Intercranial neural interfaces are known, see U.S. Pat. No. 7,548,775, as well as cranial lead anchoring systems that use a threaded attachment within a skull burr hole, see, for example, U.S. Pat. Nos. 7,302,298 and 6,210,417.
Recent progress in CMOS and MEMS technologies has enabled the development of sensor-based mixed-signal circuits and self-powered microsystems such as sensor networks, portable devices, and implantable systems for improving health care. For implantable microsystems powered or recharged by inductively-coupled link, generation of supply-independent voltage/current references can be important. Previous approaches using subthreshold MOSFETs can be complicated, may consume a large area, and may not be optimized for implantable microsystems. Also, these energy-constrained mixed-signal systems, especially for implantable devices, may utilize an analog to digital converter (ADC) that provides low supply-voltage operation (<1V) with a moderate conversion speed (few tens of kS/s) and resolution of, for example, 8 bits. Typically, these devices have used relatively larger feature sizes (>0.25 μm) in order to achieve better 1/f noise performance of a preamplifier; thus, lowering the supply voltage below the threshold voltage (˜0.5V) is challenging. Recently, ADCs have been realized in smaller feature sizes (<90 nm) to operate at less than 0.5V. However, applying a large voltage to sampling transistors for rail-to-rail operation may induce potential reliability problems and may require a large area.